Swap Misplaced cells in R? - r

Question

I have a huge database (more than 65M of rows) and I noticed that some cells are misplaced. As an example, let's say I have this:
library("tidyverse")
DATA <- tribble(
~SURNAME,~NAME,~STATE,~COUNTRY,
'Smith','Emma','California','USA',
'Johnson','Oliia','Texas','USA',
'Williams','James','USA','California',
'Jones','Noah','Pennsylvania','USA',
'Williams','Liam','Illinois','USA',
'Brown','Sophia','USA','Louisiana',
'Daves','Evelyn','USA','Oregon',
'Miller','Jacob','New Mexico','USA',
'Williams','Lucas','Connecticut','USA',
'Daves','John','California','USA',
'Jones','Carl','USA','Illinois'
)
=====
> DATA
# A tibble: 11 x 4
SURNAME NAME STATE COUNTRY
<chr> <chr> <chr> <chr>
1 Smith Emma California USA
2 Johnson Oliia Texas USA
3 Williams James USA California
4 Jones Noah Pennsylvania USA
5 Williams Liam Illinois USA
6 Brown Sophia USA Louisiana
7 Daves Evelyn USA Oregon
8 Miller Jacob New Mexico USA
9 Williams Lucas Connecticut USA
10 Daves John California USA
11 Jones Carl USA Illinois
As you can see, the Country and State are misplaced in some rows, how can I efficiently swap those ones?
Kind Regards,
Luiz.

Answer 1

Using data.table and the in-built state.name vector:
setDT(DATA)
DATA[COUNTRY %in% state.name, `:=`(COUNTRY = STATE, STATE = COUNTRY)]
DATA
# SURNAME NAME STATE COUNTRY
# 1: Smith Emma California USA
# 2: Johnson Oliia Texas USA
# 3: Williams James California USA
# 4: Jones Noah Pennsylvania USA
# 5: Williams Liam Illinois USA
# 6: Brown Sophia Louisiana USA
# 7: Daves Evelyn Oregon USA
# 8: Miller Jacob New Mexico USA
# 9: Williams Lucas Connecticut USA
# 10: Daves John California USA
# 11: Jones Carl Illinois USA

Answer 2

Check this solution (it assumes that COUNTRY column is in ISO3 format e.g. MEX, CAN):
DATA %>%
mutate(
COUNTRY_TMP = if_else(str_detect(COUNTRY, '[A-Z]{3}'), COUNTRY, STATE),
STATE = if_else(str_detect(COUNTRY, '[A-Z]{3}'), STATE, COUNTRY),
COUNTRY = COUNTRY_TMP
) %>%
select(-COUNTRY_TMP)

Answer 3

Assuming all country names are followed ISO3 format, we can first install the countrycode package. In this package, there is a data frame called codelist with a column iso3c with the ISO3 country names. We can use that as follows to swap the country name.
library(tidyverse)
library(countrycode)
DATA2 <- DATA %>%
mutate(STATE2 = ifelse(STATE %in% codelist$iso3c &
!COUNTRY %in% codelist$iso3c, COUNTRY, STATE),
COUNTRY2 = ifelse(!STATE %in% codelist$iso3c &
COUNTRY %in% codelist$iso3c, COUNTRY, STATE)) %>%
select(-STATE, -COUNTRY) %>%
rename(STATE = STATE2, COUNTRY = COUNTRY2)
DATA2
# # A tibble: 11 x 4
# SURNAME NAME STATE COUNTRY
# <chr> <chr> <chr> <chr>
# 1 Smith Emma California USA
# 2 Johnson Oliia Texas USA
# 3 Williams James California USA
# 4 Jones Noah Pennsylvania USA
# 5 Williams Liam Illinois USA
# 6 Brown Sophia Louisiana USA
# 7 Daves Evelyn Oregon USA
# 8 Miller Jacob New Mexico USA
# 9 Williams Lucas Connecticut USA
# 10 Daves John California USA
# 11 Jones Carl Illinois USA